Relaxed Lasso about glmnet.jl HOT 9 OPEN

Thanks for the great package!
Would it be possible to change 'CompressedPredictorMatrix' to a mutable struct? This would allow modifying the predicted values and implementing the relaxed lasso.

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I had a quick look through how this is implemented in the R package, and it looks like the logic for the relaxed option sits in the R code, rather than in the core fortran library. So unfortunately it looks like we can't simply access the relaxed option from the core compiled library, instead this R logic would need to be duplicated into the Julia package which is a bigger undertaking.

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I see, That means it’s likely to be faster in the julia version

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I think that should be fine, although it might be better to update the code to use a generic sparse matrix instead rather than the custom struct. I'm not too familiar with the internals of the package but it feels like that should be possible?

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Yes, a sparse matrix might be better to save the parameters. Regarding the struct, I think line 90 should be changed to a mutable struct: https://github.com/JuliaStats/GLMNet.jl/blob/master/src/GLMNet.jl
Or is there any other possibility to change and save the values? I want to modify the parameters and then use them with GLMNet.predict()

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Or is there any other possibility to change and save the values?

You may be able to use Setfield.jl or Accessors.jl to easily update the GLMNetPath with new coefficients, something like

new_path = @set path.betas = new_betas

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Nice, thanks for the tip!

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@AdaemmerP did you have any luck implementing the relaxed Lasso?

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@azev77 Yes, I was able to implement but within a time series framework (https://github.com/AdaemmerP/DetectSparsity/blob/main/CaseStudies/Functions.jl, lines 337 - 501). I also used the Lasso.jl package for it.

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